After a question in the TAC about whether the same genes drive the TAI, I decided to use pMatrix and check which genes drive the high (young) TAI in the gametes.
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(myTAI)
library(see)
Non-transformed
Ec_PES_32m <-
readr::read_csv(file = "data/Ec_PES_32m.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f <-
readr::read_csv(file = "data/Ec_PES_25f.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
sqrt-tranformed
Ec_PES_32m.sqrt <-
readr::read_csv(file = "data/Ec_PES_32m.sqrt.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f.sqrt <-
readr::read_csv(file = "data/Ec_PES_25f.sqrt.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
log2-tranformed
Ec_PES_32m.log2 <-
readr::read_csv(file = "data/Ec_PES_32m.log2.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f.log2 <-
readr::read_csv(file = "data/Ec_PES_25f.log2.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
rank-tranformed
Ec_PES_32m.rank <-
readr::read_csv(file = "data/Ec_PES_32m.rank.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f.rank <-
readr::read_csv(file = "data/Ec_PES_25f.rank.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
rlog-tranformed
Ec_PES_32m.rlog <-
readr::read_csv(file = "data/Ec_PES_32m.rlog.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f.rlog <-
readr::read_csv(file = "data/Ec_PES_25f.rlog.csv")
## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
unicellular_pMat <- function(PES, unicell_names = c("meiospore", "gamete", "mitospore"), dropzero = F, ordered_stages, top_genes = F, n_genes = 100, stage_select = "gamete"){
if(!is.data.frame(PES))
stop("PES is not a dataframe")
stage_select <- rlang::sym(stage_select)
PES_filt <- PES %>%
dplyr::select(1, 2, all_of(unicell_names))
if(dropzero){
PES_filt <- PES_filt %>%
dplyr::filter(rowSums(!select(., all_of(unicell_names))) == 0)
}
PES_filt <- PES_filt %>%
myTAI::pMatrix() %>%
tibble::as_tibble(rownames = "GeneID")
# in case one wants to select the top N genes.
if(top_genes){
PES_filt <- PES_filt %>%
dplyr::slice_max({{stage_select}}, n = n_genes)
}
PES_filt <- PES_filt %>%
tidyr::pivot_longer(!GeneID, names_to = "Stage", values_to = "pTAI")
# make factor
PES_filt$Stage <- base::factor(PES_filt$Stage, ordered_stages)
return(PES_filt)
}
unicellular_pMat_plot <- function(PES,
unicell_names = c("meiospore", "gamete", "mitospore"),
n_genes = 100,
stage_select = "gamete",
transformation = "log2",
alpha = 0,
dropzero = F,
ordered_stages) {
top_pTAI <- PES %>%
unicellular_pMat(dropzero = dropzero, ordered_stages = ordered_stages, top_genes = TRUE, stage_select = stage_select, n_genes = n_genes)
PES_filt <- unicellular_pMat(PES = PES, unicell_names = unicell_names, ordered_stages = ordered_stages)
# Rest of your function logic
# ...
# transformation
f <- match.fun(transformation)
plot_re <- PES_filt %>%
ggplot2::ggplot(aes(y = f(pTAI + alpha), x = Stage, fill = Stage)) +
see::geom_violinhalf(trim = TRUE, width=1.2, alpha = 0.8) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white")) +
ggplot2::geom_line(
data = top_pTAI,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.2,
colour = "#3C5488FF") +
ggplot2::geom_point(
data = top_pTAI,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.3,
size = 0.7,
colour = "#3C5488FF") +
ggplot2::ylab(paste0(transformation, "(pTAI)")) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position="none") +
ggplot2::coord_flip()
return(plot_re)
}
unicellular_pMat_plot_all <- function(PES,
unicell_names = c("meiospore", "gamete", "mitospore"),
n_genes = 100,
transformation = "log2",
alpha = 0,
dropzero = F,
ordered_stages) {
top_pTAI_1 <- PES %>%
unicellular_pMat(dropzero = dropzero, ordered_stages = ordered_stages, top_genes = TRUE, stage_select = unicell_names[[1]], n_genes = n_genes)
top_pTAI_2 <- PES %>%
unicellular_pMat(dropzero = dropzero, ordered_stages = ordered_stages, top_genes = TRUE, stage_select = unicell_names[[2]], n_genes = n_genes)
top_pTAI_3 <- PES %>%
unicellular_pMat(dropzero = dropzero, ordered_stages = ordered_stages, top_genes = TRUE, stage_select = unicell_names[[3]], n_genes = n_genes)
top_pTAI_all <- top_pTAI_1 %>%
inner_join(top_pTAI_2) %>%
inner_join(top_pTAI_3)
PES_filt <- unicellular_pMat(PES = PES, unicell_names = unicell_names, ordered_stages = ordered_stages)
# Rest of your function logic
# ...
# transformation
f <- match.fun(transformation)
plot_re <- PES_filt %>%
ggplot2::ggplot(aes(y = f(pTAI + alpha), x = Stage, fill = Stage)) +
see::geom_violinhalf(trim = TRUE, width=1.2, alpha = 0.5) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "#E64B35FF", "#00A087FF")) +
ggplot2::geom_line(
data = top_pTAI_1,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.1,
size = 0.3,
colour = "#3C5488FF") +
ggplot2::geom_point(
data = top_pTAI_1,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.1,
size = 0.7,
colour = "#3C5488FF") +
ggplot2::geom_line(
data = top_pTAI_2,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.1,
size = 0.3,
colour = "#E64B35FF") +
ggplot2::geom_point(
data = top_pTAI_3,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.1,
size = 0.7,
colour = "#E64B35FF") +
ggplot2::geom_line(
data = top_pTAI_3,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.1,
size = 0.3,
colour = "#00A087FF") +
ggplot2::geom_point(
data = top_pTAI_3,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.1,
size = 0.7,
colour = "#00A087FF") +
ggplot2::geom_line(
data = top_pTAI_all,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.5,
colour = "black") +
ggplot2::ylab(paste0(transformation, "(pTAI)")) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position="none") +
ggplot2::coord_flip()
return(plot_re)
}
ordered_stages <- c("gamete", "meiospore", "mitospore")
unicellular_pMat_plot(Ec_PES_32m, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "raw(TPM)")
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "raw(TPM)")
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.sqrt, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)")
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f.sqrt, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)")
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.log2, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "log2(TPM+1)")
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f.log2, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "log2(TPM+1)")
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.rank, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "rank(TPM)")
unicellular_pMat_plot(Ec_PES_25f.rank, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "rank(TPM)")
unicellular_pMat_plot(Ec_PES_32m.rlog, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 9018 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 10 rows containing missing values (`geom_point()`).
unicellular_pMat_plot(Ec_PES_25f.rlog, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 9203 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 7 rows containing missing values (`geom_point()`).
unicellular_pMat_plot_all(Ec_PES_32m, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "raw(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot_all(Ec_PES_25f, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "raw(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot_all(Ec_PES_32m.sqrt, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot_all(Ec_PES_25f.sqrt, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot_all(Ec_PES_32m.log2, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "log2(TPM+1)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot_all(Ec_PES_25f.log2, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "log2(TPM+1)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot_all(Ec_PES_32m.rank, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "rank(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
unicellular_pMat_plot_all(Ec_PES_25f.rank, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "rank(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
unicellular_pMat_plot_all(Ec_PES_32m.rlog, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "rlog(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 9018 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 21 rows containing missing values (`geom_line()`).
## Warning: Removed 21 rows containing missing values (`geom_point()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 3 rows containing missing values (`geom_point()`).
## Warning: Removed 1 row containing missing values (`geom_line()`).
## Warning: Removed 3 rows containing missing values (`geom_point()`).
unicellular_pMat_plot_all(Ec_PES_25f.rlog, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "rlog(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 9203 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 23 rows containing missing values (`geom_line()`).
## Warning: Removed 23 rows containing missing values (`geom_point()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).
## Warning: Removed 4 rows containing missing values (`geom_line()`).
## Warning: Removed 4 rows containing missing values (`geom_point()`).
unicellular_pMat_plot(Ec_PES_32m, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "raw(TPM)")
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "raw(TPM)")
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.sqrt, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)")
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f.sqrt,
dropzero = TRUE,
ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)")
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.log2, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "log2(TPM+1)")
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f.log2, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "log2(TPM+1)")
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.rank, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "rank(TPM)")
unicellular_pMat_plot(Ec_PES_25f.rank, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "rank(TPM)")
unicellular_pMat_plot(Ec_PES_32m.rlog, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 9018 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 10 rows containing missing values (`geom_point()`).
unicellular_pMat_plot(Ec_PES_25f.rlog, dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 9203 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 7 rows containing missing values (`geom_point()`).
unicellular_pMat_plot(Ec_PES_25f.sqrt,stage_select = "mitospore", ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "white", "#3C5488FF"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.sqrt,stage_select = "mitospore", ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "white", "#3C5488FF"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f.sqrt,stage_select = "meiospore", ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "#3C5488FF", "white"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.sqrt,stage_select = "meiospore", ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "#3C5488FF", "white"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f.sqrt,stage_select = "mitospore", dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "white", "#3C5488FF"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.sqrt,stage_select = "mitospore", dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "white", "#3C5488FF"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_25f.sqrt,stage_select = "meiospore", dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "#3C5488FF", "white"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14016 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot(Ec_PES_32m.sqrt,stage_select = "meiospore", dropzero = TRUE, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)") +
ggplot2::scale_fill_manual(values = c("white", "#3C5488FF", "white"))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot <- function(PES,
multicell_names = c("immGA", "matGA", "oldGA", "earlyPSP", "immPSP", "matPSP"),
n_genes = 100,
stage_select = "immGA",
transformation = "log2",
alpha = 0,
dropzero = F,
ordered_stages) {
top_pTAI <- PES %>%
unicellular_pMat(dropzero = dropzero, unicell_names = multicell_names, ordered_stages = ordered_stages, top_genes = TRUE, stage_select = stage_select, n_genes = n_genes)
PES_filt <- unicellular_pMat(PES = PES, unicell_names = multicell_names, ordered_stages = ordered_stages)
# transformation
f <- match.fun(transformation)
plot_re <- PES_filt %>%
ggplot2::ggplot(aes(y = f(pTAI + alpha), x = Stage, fill = Stage)) +
see::geom_violinhalf(trim = TRUE, width=1.2, alpha = 0.8) +
ggplot2::geom_line(
data = top_pTAI,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.2,
colour = "#3C5488FF") +
ggplot2::geom_point(
data = top_pTAI,
aes(y = f(pTAI + alpha), x = Stage, group = GeneID),
alpha = 0.3,
size = 0.7,
colour = "#3C5488FF") +
ggplot2::ylab(paste0(transformation, "(pTAI)")) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position="none") +
ggplot2::coord_flip()
return(plot_re)
}
ordered_stages = c("immGA", "matGA", "oldGA", "earlyPSP", "immPSP", "matPSP")
multicellular_pMat_plot(Ec_PES_32m.sqrt, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)")
## Warning: Removed 2817 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f.sqrt, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)")
## Warning: Removed 2285 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_32m, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "raw(TPM)")
## Warning: Removed 2817 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "raw(TPM)")
## Warning: Removed 2285 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_32m.rank, stage_select = "immGA", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "rank(TPM)")
multicellular_pMat_plot(Ec_PES_25f.rank, stage_select = "immGA", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "rank(TPM)")
multicellular_pMat_plot(Ec_PES_32m.log2, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "log2(TPM+1)")
## Warning: Removed 2817 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f.log2, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "log2(TPM+1)")
## Warning: Removed 2285 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_32m.rlog, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 5084 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f.rlog, stage_select = "immGA", ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 4756 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 8 rows containing missing values (`geom_point()`).
ordered_stages = c("immGA", "matGA", "oldGA", "earlyPSP", "immPSP", "matPSP")
multicellular_pMat_plot(Ec_PES_32m.sqrt, stage_select = "immGA", ordered_stages = ordered_stages, dropzero = TRUE) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)")
## Warning: Removed 2817 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f.sqrt, stage_select = "immGA", ordered_stages = ordered_stages, dropzero = TRUE) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)")
## Warning: Removed 2285 rows containing non-finite values (`stat_ydensity()`).
What about with gametes??
ordered_stages = c("gamete", "immGA", "matGA", "oldGA", "earlyPSP", "immPSP", "matPSP")
multicellular_pMat_plot(Ec_PES_32m.sqrt, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)")
## Warning: Removed 3718 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f.sqrt, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)")
## Warning: Removed 3542 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_32m, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "raw(TPM)")
## Warning: Removed 3718 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "raw(TPM)")
## Warning: Removed 3542 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_32m.rank, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "rank(TPM)")
multicellular_pMat_plot(Ec_PES_25f.rank, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "rank(TPM)")
multicellular_pMat_plot(Ec_PES_32m.log2, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "log2(TPM+1)")
## Warning: Removed 3718 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f.log2, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "log2(TPM+1)")
## Warning: Removed 3542 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_32m.rlog, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 7015 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 4 rows containing missing values (`geom_line()`).
## Warning: Removed 20 rows containing missing values (`geom_point()`).
multicellular_pMat_plot(Ec_PES_25f.rlog, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "rlog(TPM)")
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning in FUN(X[[i]], ...): NaNs produced
## Warning: Removed 7023 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 1 row containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).
ordered_stages = c("gamete", "immGA", "matGA", "oldGA", "earlyPSP", "immPSP", "matPSP")
multicellular_pMat_plot(Ec_PES_32m.sqrt, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages, dropzero = TRUE) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec32m",
subtitle = "sqrt(TPM)")
## Warning: Removed 3718 rows containing non-finite values (`stat_ydensity()`).
multicellular_pMat_plot(Ec_PES_25f.sqrt, stage_select = "gamete", multicell_names = ordered_stages, ordered_stages = ordered_stages, dropzero = TRUE) +
ggplot2::scale_fill_manual(values = c("#3C5488FF", "white", "white", "white", "white", "white", "white", "white", "white")) +
ggplot2::labs(title = "Ec25f",
subtitle = "sqrt(TPM)")
## Warning: Removed 3542 rows containing non-finite values (`stat_ydensity()`).
Non-transformed
Ec_PES_32m_denoised <-
readr::read_csv(file = "data/Ec_PES_32m_denoised.csv")
## Rows: 2719 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f_denoised <-
readr::read_csv(file = "data/Ec_PES_25f_denoised.csv")
## Rows: 2719 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
sqrt-tranformed
Ec_PES_32m.sqrt_denoised <-
readr::read_csv(file = "data/Ec_PES_32m.sqrt_denoised.csv")
## Rows: 2719 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f.sqrt_denoised <-
readr::read_csv(file = "data/Ec_PES_25f.sqrt_denoised.csv")
## Rows: 2719 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
log2-tranformed
Ec_PES_32m.log2_denoised <-
readr::read_csv(file = "data/Ec_PES_32m.log2_denoised.csv")
## Rows: 2719 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ec_PES_25f.log2_denoised <-
readr::read_csv(file = "data/Ec_PES_25f.log2_denoised.csv")
## Rows: 2719 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): PS, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, immPSP, matP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
ordered_stages <- c("gamete", "meiospore", "mitospore")
unicellular_pMat_plot(Ec_PES_32m_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m denoised",
subtitle = "raw(TPM)")
unicellular_pMat_plot(Ec_PES_25f_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f denoised",
subtitle = "raw(TPM)")
unicellular_pMat_plot(Ec_PES_32m.sqrt_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m denoised",
subtitle = "sqrt(TPM)")
unicellular_pMat_plot(Ec_PES_25f.sqrt_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f denoised",
subtitle = "sqrt(TPM)")
unicellular_pMat_plot(Ec_PES_32m.log2_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m denoised",
subtitle = "log2(TPM+1)")
unicellular_pMat_plot(Ec_PES_25f.log2_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f denoised",
subtitle = "log2(TPM+1)")
unicellular_pMat_plot_all(Ec_PES_32m, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m",
subtitle = "raw(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Warning: Removed 14980 rows containing non-finite values (`stat_ydensity()`).
unicellular_pMat_plot_all(Ec_PES_32m_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m denoised",
subtitle = "raw(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
unicellular_pMat_plot_all(Ec_PES_25f_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f denoised",
subtitle = "raw(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
unicellular_pMat_plot_all(Ec_PES_32m.sqrt_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m denoised",
subtitle = "sqrt(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
unicellular_pMat_plot_all(Ec_PES_25f.sqrt_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f denoised",
subtitle = "sqrt(TPM)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
unicellular_pMat_plot_all(Ec_PES_32m.log2_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec32m denoised",
subtitle = "log2(TPM+1)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
unicellular_pMat_plot_all(Ec_PES_25f.log2_denoised, ordered_stages = ordered_stages) +
ggplot2::labs(title = "Ec25f denoised",
subtitle = "log2(TPM+1)")
## Joining with `by = join_by(GeneID, Stage, pTAI)`
## Joining with `by = join_by(GeneID, Stage, pTAI)`
ordered_stages <- c("gamete", "meiospore", "mitospore")
intersection_genes(Ec_PES_32m_denoised, ordered_stages = ordered_stages) %>% nrow()
## [1] 23
intersection_genes(Ec_PES_25f_denoised, ordered_stages = ordered_stages) %>% nrow()
## [1] 14
intersection_genes(Ec_PES_32m.sqrt_denoised, ordered_stages = ordered_stages) %>% nrow()
## [1] 18
intersection_genes(Ec_PES_25f.sqrt_denoised, ordered_stages = ordered_stages) %>% nrow()
## [1] 16
intersection_genes(Ec_PES_32m.log2_denoised, ordered_stages = ordered_stages) %>% nrow()
## [1] 35
intersection_genes(Ec_PES_25f.log2_denoised, ordered_stages = ordered_stages) %>% nrow()
## [1] 30
# intersection_genes(Ec_PES_32m_denoised, ordered_stages = ordered_stages) %>% select(1) %>% write_csv("data/pTAI/Ec_PES_32m_denoised_unicell.csv")
# intersection_genes(Ec_PES_25f_denoised, ordered_stages = ordered_stages) %>% select(1) %>% write_csv("data/pTAI/Ec_PES_25f_denoised_unicell.csv")
# intersection_genes(Ec_PES_32m.sqrt_denoised, ordered_stages = ordered_stages) %>% select(1) %>% write_csv("data/pTAI/Ec_PES_32m.sqrt_denoised_unicell.csv")
# intersection_genes(Ec_PES_25f.sqrt_denoised, ordered_stages = ordered_stages) %>% select(1) %>% write_csv("data/pTAI/Ec_PES_25f.sqrt_denoised_unicell.csv")
# intersection_genes(Ec_PES_32m.log2_denoised, ordered_stages = ordered_stages) %>% select(1) %>% write_csv("data/pTAI/Ec_PES_32m.log2_denoised_unicell.csv")
# intersection_genes(Ec_PES_25f.log2_denoised, ordered_stages = ordered_stages) %>% select(1) %>% write_csv("data/pTAI/Ec_PES_25f.log2_denoised_unicell.csv")
We are left with a similar amount of genes.
Get session info.
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.2 (2022-10-31)
## os macOS Big Sur ... 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/Berlin
## date 2024-01-16
## pandoc 3.1.6.2 @ /usr/local/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## bit 4.0.5 2022-11-15 [1] CRAN (R 4.2.0)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
## bslib 0.5.1 2023-08-11 [1] CRAN (R 4.2.0)
## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
## codetools 0.2-19 2023-02-01 [1] CRAN (R 4.2.0)
## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
## digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.0)
## dplyr * 1.1.3 2023-09-03 [1] CRAN (R 4.2.0)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
## evaluate 0.22 2023-09-29 [1] CRAN (R 4.2.2)
## fansi 1.0.5 2023-10-08 [1] CRAN (R 4.2.2)
## farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.0)
## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.0)
## foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.0)
## fs 1.6.3 2023-07-20 [1] CRAN (R 4.2.0)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
## ggplot2 * 3.4.4 2023-10-12 [1] CRAN (R 4.2.2)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
## gtable 0.3.4 2023-08-21 [1] CRAN (R 4.2.0)
## hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0)
## htmltools 0.5.6.1 2023-10-06 [1] CRAN (R 4.2.2)
## htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
## httpuv 1.6.11 2023-05-11 [1] CRAN (R 4.2.2)
## insight 0.19.6 2023-10-12 [1] CRAN (R 4.2.2)
## iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
## jsonlite 1.8.7 2023-06-29 [1] CRAN (R 4.2.0)
## knitr 1.44 2023-09-11 [1] CRAN (R 4.2.0)
## labeling 0.4.3 2023-08-29 [1] CRAN (R 4.2.0)
## later 1.3.1 2023-05-02 [1] CRAN (R 4.2.2)
## lattice 0.21-9 2023-10-01 [1] CRAN (R 4.2.2)
## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.2.0)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
## Matrix 1.5-4.1 2023-05-18 [1] CRAN (R 4.2.0)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
## myTAI * 1.0.1.9000 2023-12-07 [1] Github (drostlab/myTAI@27a2639)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
## pkgbuild 1.4.2 2023-06-26 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
## pkgload 1.3.3 2023-09-22 [1] CRAN (R 4.2.0)
## prettyunits 1.2.0 2023-09-24 [1] CRAN (R 4.2.0)
## processx 3.8.2 2023-06-30 [1] CRAN (R 4.2.0)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
## promises 1.2.1 2023-08-10 [1] CRAN (R 4.2.2)
## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.0)
## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.2)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
## Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.2.0)
## readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.0)
## remotes 2.4.2.1 2023-07-18 [1] CRAN (R 4.2.2)
## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
## rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.2.2)
## rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.2.0)
## sass 0.4.7 2023-07-15 [1] CRAN (R 4.2.0)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## see * 0.8.0 2023-06-05 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
## shiny 1.7.5.1 2023-10-14 [1] CRAN (R 4.2.2)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.0)
## timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.0)
## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.2)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.2.2 2023-07-06 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## vctrs 0.6.4 2023-10-12 [1] CRAN (R 4.2.2)
## vroom 1.6.4 2023-10-02 [1] CRAN (R 4.2.2)
## withr 2.5.1 2023-09-26 [1] CRAN (R 4.2.0)
## xfun 0.40 2023-08-09 [1] CRAN (R 4.2.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
##
## ──────────────────────────────────────────────────────────────────────────────